{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 数据结构\n",
    "* Array产生\n",
    "* Array性质\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy 是一个为python提供高性能向量、矩阵和高维数据结构的科学计算库。Numpy主要运算对象是多维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "\n",
    "Image(filename='list_set_dict.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type([3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list([3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(list([3,4,5,6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[3,4,5,6]==[4,5,6,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[3,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c', 1]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['a','b','c',1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6, 'hello']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[3,4,5,6]\n",
    "l.append('hello')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'add'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-2fca0db019da>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'add'"
     ]
    }
   ],
   "source": [
    "l.add('hello')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 'python', 5, 6, 'hello']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1]='python'\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5, 6, 'hello']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.remove('python')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=l.remove(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[3,4,5,6]\n",
    "l.remove(3)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=[]\n",
    "l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5, 6)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3,4,5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type((3,4,5,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5, 6)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple([3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tuple([3,4,5,6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3,4,5,6)==(4,5,6,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3, 4, 5, 6)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3,3,4,5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 'no')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3,4,'no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'append'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-22c62a85a7b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'"
     ]
    }
   ],
   "source": [
    "l=(3,4,5,6)\n",
    "l.append('hello')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'add'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-6c7e1c02f531>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'add'"
     ]
    }
   ],
   "source": [
    "l.add('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'remove'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-a16c39d372c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'remove'"
     ]
    }
   ],
   "source": [
    "l.remove(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-0c011061847e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'python'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "l[1]='python'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{1,2,3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type({1,2,3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4, 5, 6}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(set([3,4,5,6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{3,4,5,6}=={4,5,6,3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4, 5, 6}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{3,3,4,5,6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4, 5, 6, 'hello'}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l={3,4,5,6}\n",
    "l.add('hello')\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 5, 6, 'hello'}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.remove(4)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'hello' in l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 3, 'b': 4, 'c': 5, 'd': 6}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'a':3,'b':4,'c':5,'d':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type({'a':3,'b':4,'c':5,'d':6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'a':3,'c':5,'d':6,'b':4}=={'a':3,'b':4,'c':5,'d':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 3, 'b': 4, 'c': 5, 'd': 6}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'a':3,'a':3,'b':4,'c':5,'d':6}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 3, 'b': 4, 'c': 5, 'd': 6}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary={'a':3,'b':4,'c':5,'d':6}\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary.get('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 4, 'c': 5, 'd': 6}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary['a']=100\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 100, 'b': 4, 'c': 5, 'd': 6, 'f': 'hello'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary.update({'f':'hello'})\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': 4, 'c': 5, 'd': 6, 'f': 'hello'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary.pop('a')\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'b' in dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b', 'c', 'd', 'f']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dictionary.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('b', 4), ('c', 5), ('d', 6), ('f', 'hello')]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dictionary.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array 产生\n",
    "* 通过list\n",
    "* 通过tuple\n",
    "* 通过list of list\n",
    "* 通过list of tuple\n",
    "* 通过function\n",
    "    * arange - 构建等差数组\n",
    "    * linspace - 等差数组\n",
    "    * logspace - 等比数组\n",
    "    * zeros\n",
    "    * ones\n",
    "    * random - 随机数组\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename='array.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([3,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([3,2],ndmin=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array((3,2),ndmin=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 1],\n",
       "       [3, 4, 1]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,0,1],[3,4,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 1],\n",
       "       [3, 4, 1]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([(1,0,1),(3,4,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 1.],\n",
       "       [3., 4., 1.]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,0,1],[3,4,1]],dtype='float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 1],\n",
       "       [3, 4, 1]], dtype=int64)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,0,1],[3,4,1]],dtype='int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1', '0', '1'],\n",
       "       ['3', '4', '1']], dtype='<U1')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,0,1],[3,4,1]],dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 7, 9],\n",
       "        [5, 9, 3],\n",
       "        [1, 9, 9]],\n",
       "\n",
       "       [[1, 0, 1],\n",
       "        [3, 4, 1],\n",
       "        [5, 6, 7]]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[[1,7,9],[5,9,3],[1,9,9]],[[1,0,1],[3,4,1],[5,6,7]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,50,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 15, 20, 25],\n",
       "       [30, 35, 40, 45]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,50,5).reshape(2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 15, 20, 25],\n",
       "       [30, 35, 40, 45]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,50,5).reshape(2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 20., 30., 40.])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,50,10,dtype='float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(15).reshape(3,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.4, 0.8, 1.2, 1.6, 2. ])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,2,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   2.,   4.,   8.,  16.],\n",
       "       [ 32.,  64., 128., 256., 512.]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(0,9,10,base=2).reshape(2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.57248852, 0.73314253, 0.51901163, 0.77088391],\n",
       "       [0.56885799, 0.46570988, 0.34268891, 0.06820935],\n",
       "       [0.37792418, 0.07962608, 0.98281711, 0.18161285]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.17022005e-01, 7.20324493e-01, 1.14374817e-04, 3.02332573e-01],\n",
       "       [1.46755891e-01, 9.23385948e-02, 1.86260211e-01, 3.45560727e-01],\n",
       "       [3.96767474e-01, 5.38816734e-01, 4.19194514e-01, 6.85219500e-01]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "np.random.random((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3190391 , -0.24937038,  1.46210794, -2.06014071],\n",
       "       [-0.3224172 , -0.38405435,  1.13376944, -1.09989127],\n",
       "       [-0.17242821, -0.87785842,  0.04221375,  0.58281521]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[44, 33, 17, 36, 35],\n",
       "       [32, 19, 13, 49, 33]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(10,50,10).reshape(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array性质"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.arange(10).reshape(2,-1)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 2., 3., 4.],\n",
       "       [5., 6., 7., 8., 9.]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=a.astype('float64')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 5.],\n",
       "       [1., 6.],\n",
       "       [2., 7.],\n",
       "       [3., 8.],\n",
       "       [4., 9.]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 5.],\n",
       "       [1., 6.],\n",
       "       [2., 7.],\n",
       "       [3., 8.],\n",
       "       [4., 9.]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rollaxis(a,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课后作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机产生一个3维（4,3,5）的数组。数值为0到1之间。 seed设为0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "产生一个2维（3，5）的等差数组，数字为0到14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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